{"total":61,"rows":[{"id":"2110.12088","title":"Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations","created":"2021-10-22","cited_by_count":256,"area":null},{"id":"2106.15147","title":"SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption","created":"2021-06-29","cited_by_count":156,"area":null},{"id":"2108.01624","title":"Large-Scale Differentially Private BERT","created":"2021-08-03","cited_by_count":118,"area":null},{"id":"2103.07756","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","created":"2021-03-13","cited_by_count":102,"area":null},{"id":"2202.14026","title":"Robust Training under Label Noise by Over-parameterization","created":"2022-02-28","cited_by_count":93,"area":null},{"id":"2106.04149","title":"To Smooth or Not? When Label Smoothing Meets Noisy Labels","created":"2021-06-08","cited_by_count":70,"area":null},{"id":"2102.05291","title":"Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels","created":"2021-02-10","cited_by_count":65,"area":null},{"id":"2110.06283","title":"Detecting Corrupted Labels Without Training a Model to Predict","created":"2021-10-12","cited_by_count":58,"area":null},{"id":"2208.11351","title":"Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization","created":"2022-08-24","cited_by_count":33,"area":null},{"id":"2206.07181","title":"To Aggregate or Not? Learning with Separate Noisy Labels","created":"2022-06-14","cited_by_count":29,"area":null},{"id":"2202.05613","title":"CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning","created":"2022-02-11","cited_by_count":24,"area":null},{"id":"2203.01785","title":"On Learning Contrastive Representations for Learning with Noisy Labels","created":"2022-03-03","cited_by_count":21,"area":null},{"id":"2207.13378","title":"Identifying Hard Noise in Long-Tailed Sample Distribution","created":"2022-07-27","cited_by_count":19,"area":null},{"id":"2104.01493","title":"Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond","created":"2021-04-03","cited_by_count":16,"area":null},{"id":"2205.12702","title":"Detecting Label Errors by using Pre-Trained Language Models","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2309.08825","title":"Distributionally Robust Post-hoc Classifiers under Prior Shifts","created":"2023-09-16","cited_by_count":16,"area":null},{"id":"2106.06199","title":"LocoProp: Enhancing BackProp via Local Loss Optimization","created":"2021-06-11","cited_by_count":15,"area":null},{"id":"2211.05764","title":"DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems","created":"2022-11-09","cited_by_count":14,"area":null},{"id":"2103.00243","title":"Searching for Robustness: Loss Learning for Noisy Classification Tasks","created":"2021-02-27","cited_by_count":13,"area":null},{"id":"2208.09378","title":"Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels","created":"2022-08-19","cited_by_count":13,"area":null},{"id":"2111.05428","title":"Constrained Instance and Class Reweighting for Robust Learning under Label Noise","created":"2021-11-09","cited_by_count":12,"area":null},{"id":"2106.09920","title":"Being Properly Improper","created":"2021-06-18","cited_by_count":11,"area":null},{"id":"2205.15235","title":"Non-convex online learning via algorithmic equivalence","created":"2022-05-30","cited_by_count":11,"area":null},{"id":"2302.03806","title":"SLaM: Student-Label Mixing for Distillation with Unlabeled Examples","created":"2023-02-08","cited_by_count":10,"area":"distill"},{"id":"2402.11173","title":"How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization","created":"2024-02-17","cited_by_count":10,"area":null},{"id":"2303.11562","title":"Dynamics-Aware Loss for Learning with Label Noise","created":"2023-03-21","cited_by_count":9,"area":null},{"id":"2211.05232","title":"MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid","created":"2022-11-02","cited_by_count":8,"area":null},{"id":"2102.05140","title":"Locally Adaptive Label Smoothing for Predictive Churn","created":"2021-02-09","cited_by_count":7,"area":null},{"id":"2203.01859","title":"Robust PAC$^m$: Training Ensemble Models Under Misspecification and Outliers","created":"2022-03-03","cited_by_count":6,"area":null},{"id":"2211.02765","title":"Clustering above Exponential Families with Tempered Exponential Measures","created":"2022-11-04","cited_by_count":6,"area":null},{"id":"2301.08970","title":"The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making","created":"2023-01-21","cited_by_count":6,"area":null},{"id":"2302.07321","title":"On Classification-Calibration of Gamma-Phi Losses","created":"2023-02-14","cited_by_count":6,"area":null},{"id":"2501.18581","title":"Bias-variance decompositions: the exclusive privilege of Bregman divergences","created":"2025-01-30","cited_by_count":6,"area":null},{"id":"2505.16149","title":"When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification","created":"2025-05-22","cited_by_count":5,"area":"mllm"},{"id":"2102.00751","title":"Learning to Combat Noisy Labels via Classification Margins","created":"2021-02-01","cited_by_count":4,"area":null},{"id":"2102.12192","title":"Multiplicative Reweighting for Robust Neural Network Optimization","created":"2021-02-24","cited_by_count":4,"area":null},{"id":"2105.14083","title":"Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness","created":"2021-05-28","cited_by_count":4,"area":null},{"id":"2408.11598","title":"Improving Calibration by Relating Focal Loss, Temperature Scaling, and Properness","created":"2024-08-21","cited_by_count":4,"area":null},{"id":"2304.02849","title":"Logistic-Normal Likelihoods for Heteroscedastic Label Noise","created":"2023-04-06","cited_by_count":3,"area":null},{"id":"2309.04015","title":"Optimal Transport with Tempered Exponential Measures","created":"2023-09-07","cited_by_count":3,"area":null},{"id":"2401.14107","title":"Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement","created":"2024-01-25","cited_by_count":3,"area":null},{"id":"2109.02052","title":"The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description","created":"2021-09-05","cited_by_count":2,"area":null},{"id":"2502.02921","title":"Robust Reward Alignment via Hypothesis Space Batch Cutting","created":"2025-02-05","cited_by_count":2,"area":"alignment"},{"id":"2502.06753","title":"SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era","created":"2025-02-10","cited_by_count":2,"area":null},{"id":"2103.02893","title":"Lower-Bounded Proper Losses for Weakly Supervised Classification","created":"2021-03-04","cited_by_count":1,"area":null},{"id":"2205.09628","title":"What killed the Convex Booster ?","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2209.07080","title":"Layerwise Bregman Representation Learning with Applications to Knowledge Distillation","created":"2022-09-15","cited_by_count":1,"area":"distill"},{"id":"2407.06579","title":"NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification","created":"2024-07-09","cited_by_count":1,"area":null},{"id":"2410.04256","title":"Implicit to Explicit Entropy Regularization: Benchmarking ViT Fine-tuning under Noisy Labels","created":"2024-10-05","cited_by_count":1,"area":null},{"id":"2608.01223","title":"Perspectives on Tsallis Statistics for Artificial Intelligence","created":"2026-08-02","cited_by_count":1,"area":"deeprl"},{"id":"2608.02938","title":"When Should Graph Attention Be Sparse? Learning a Per-Edge Tsallis Index","created":"2026-08-03","cited_by_count":1,"area":null},{"id":"2109.03748","title":"A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training","created":"2021-09-08","cited_by_count":0,"area":null},{"id":"2109.07892","title":"Automated risk classification of colon biopsies based on semantic segmentation of histopathology images","created":"2021-09-16","cited_by_count":0,"area":null},{"id":"2301.11724","title":"Meta-Learning Mini-Batch Risk Functionals","created":"2023-01-27","cited_by_count":0,"area":null},{"id":"2302.09575","title":"Stationary Point Losses for Robust Model","created":"2023-02-19","cited_by_count":0,"area":null},{"id":"2310.05067","title":"Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance","created":"2023-10-08","cited_by_count":0,"area":null},{"id":"2505.18741","title":"MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images","created":"2025-05-24","cited_by_count":0,"area":null},{"id":"2506.04446","title":"Selective Matching Losses -- Not All Scores Are Created Equal","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2604.16562","title":"See Through the Noise: Improving Domain Generalization in Gaze Estimation","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2605.26373","title":"Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.29471","title":"Structured Proper Loss Geometries for Multiclass Classification: Theory and Controlled Empirical Evaluation","created":"2026-06-28","cited_by_count":0,"area":null}],"next_cursor":null}